Overview

Dataset statistics

Number of variables21
Number of observations8552
Missing cells1781
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory202.9 B

Variable types

Categorical7
Text5
Numeric8
DateTime1

Alerts

VoteAverage is highly overall correlated with weighted_averageHigh correlation
VoteCount is highly overall correlated with Budget and 1 other fieldsHigh correlation
Budget is highly overall correlated with VoteCount and 1 other fieldsHigh correlation
Revenue is highly overall correlated with VoteCount and 1 other fieldsHigh correlation
weighted_average is highly overall correlated with VoteAverageHigh correlation
OriginalLanguage is highly overall correlated with North America and 1 other fieldsHigh correlation
North America is highly overall correlated with OriginalLanguageHigh correlation
Asia is highly overall correlated with OriginalLanguageHigh correlation
Oceania is highly imbalanced (83.9%)Imbalance
South America is highly imbalanced (90.5%)Imbalance
Africa is highly imbalanced (93.4%)Imbalance
TagLine has 1781 (20.8%) missing valuesMissing
Budget has 3604 (42.1%) zerosZeros
Revenue has 3247 (38.0%) zerosZeros

Reproduction

Analysis started2023-11-01 22:58:34.660120
Analysis finished2023-11-01 22:58:52.873773
Duration18.21 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

OriginalLanguage
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
1
6535 
0
2017 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8552
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 6535
76.4%
0 2017
 
23.6%

Length

2023-11-01T18:58:53.101387image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T18:58:53.476063image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6535
76.4%
0 2017
 
23.6%

Most occurring characters

ValueCountFrequency (%)
1 6535
76.4%
0 2017
 
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8552
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6535
76.4%
0 2017
 
23.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6535
76.4%
0 2017
 
23.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6535
76.4%
0 2017
 
23.6%

OriginalTitle
Text

Distinct8319
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
2023-11-01T18:58:53.862014image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length104
Median length59
Mean length15.435454
Min length1

Characters and Unicode

Total characters132004
Distinct characters1846
Distinct categories20 ?
Distinct scripts14 ?
Distinct blocks20 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8104 ?
Unique (%)94.8%

Sample

1st rowInception
2nd rowBlack Widow
3rd rowThe Matrix
4th row정이
5th rowTrolls World Tour
ValueCountFrequency (%)
the 2153
 
9.2%
of 603
 
2.6%
a 288
 
1.2%
2 231
 
1.0%
in 194
 
0.8%
and 190
 
0.8%
178
 
0.8%
to 150
 
0.6%
la 113
 
0.5%
de 85
 
0.4%
Other values (8448) 19192
82.1%
2023-11-01T18:58:54.622575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
14806
 
11.2%
e 12495
 
9.5%
a 7811
 
5.9%
o 7042
 
5.3%
n 6587
 
5.0%
r 6505
 
4.9%
i 6385
 
4.8%
t 5907
 
4.5%
s 4885
 
3.7%
h 4166
 
3.2%
Other values (1836) 55415
42.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 86418
65.5%
Uppercase Letter 19020
 
14.4%
Space Separator 14825
 
11.2%
Other Letter 7914
 
6.0%
Other Punctuation 2064
 
1.6%
Decimal Number 1041
 
0.8%
Dash Punctuation 241
 
0.2%
Modifier Letter 225
 
0.2%
Nonspacing Mark 86
 
0.1%
Math Symbol 39
 
< 0.1%
Other values (10) 131
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
の 229
 
2.9%
ン 206
 
2.6%
ラ 101
 
1.3%
ス 100
 
1.3%
ト 86
 
1.1%
場 83
 
1.0%
劇 82
 
1.0%
ド 81
 
1.0%
版 81
 
1.0%
ル 80
 
1.0%
Other values (1533) 6785
85.7%
Lowercase Letter
ValueCountFrequency (%)
e 12495
14.5%
a 7811
 
9.0%
o 7042
 
8.1%
n 6587
 
7.6%
r 6505
 
7.5%
i 6385
 
7.4%
t 5907
 
6.8%
s 4885
 
5.7%
h 4166
 
4.8%
l 4156
 
4.8%
Other values (111) 20479
23.7%
Uppercase Letter
ValueCountFrequency (%)
T 2435
 
12.8%
S 1571
 
8.3%
M 1221
 
6.4%
B 1193
 
6.3%
A 1123
 
5.9%
D 1067
 
5.6%
C 1051
 
5.5%
L 996
 
5.2%
P 890
 
4.7%
H 837
 
4.4%
Other values (60) 6636
34.9%
Other Punctuation
ValueCountFrequency (%)
: 863
41.8%
' 381
18.5%
. 236
 
11.4%
! 142
 
6.9%
, 129
 
6.2%
& 115
 
5.6%
・ 46
 
2.2%
/ 32
 
1.6%
? 28
 
1.4%
： 21
 
1.0%
Other values (14) 71
 
3.4%
Nonspacing Mark
ValueCountFrequency (%)
ั 14
16.3%
์ 10
11.6%
้ 9
10.5%
ิ 7
 
8.1%
ุ 5
 
5.8%
゙ 5
 
5.8%
่ 5
 
5.8%
ं 4
 
4.7%
ู 4
 
4.7%
े 4
 
4.7%
Other values (12) 19
22.1%
Decimal Number
ValueCountFrequency (%)
2 330
31.7%
3 174
16.7%
1 150
14.4%
0 115
 
11.0%
4 69
 
6.6%
9 48
 
4.6%
5 45
 
4.3%
7 39
 
3.7%
6 34
 
3.3%
8 34
 
3.3%
Other values (2) 3
 
0.3%
Spacing Mark
ValueCountFrequency (%)
ा 13
61.9%
ी 2
 
9.5%
ि 2
 
9.5%
ం 1
 
4.8%
ो 1
 
4.8%
ை 1
 
4.8%
ி 1
 
4.8%
Close Punctuation
ValueCountFrequency (%)
] 10
27.8%
) 8
22.2%
」 7
19.4%
） 6
16.7%
〉 3
 
8.3%
』 1
 
2.8%
】 1
 
2.8%
Open Punctuation
ValueCountFrequency (%)
[ 10
27.8%
( 8
22.2%
「 7
19.4%
（ 6
16.7%
〈 3
 
8.3%
『 1
 
2.8%
【 1
 
2.8%
Math Symbol
ValueCountFrequency (%)
～ 26
66.7%
× 5
 
12.8%
+ 4
 
10.3%
~ 2
 
5.1%
＋ 1
 
2.6%
∞ 1
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 232
96.3%
〜 6
 
2.5%
― 2
 
0.8%
– 1
 
0.4%
Other Number
ValueCountFrequency (%)
½ 5
55.6%
³ 2
 
22.2%
⅓ 1
 
11.1%
² 1
 
11.1%
Final Punctuation
ValueCountFrequency (%)
’ 6
75.0%
” 1
 
12.5%
» 1
 
12.5%
Other Symbol
ValueCountFrequency (%)
☆ 4
66.7%
△ 1
 
16.7%
° 1
 
16.7%
Letter Number
ValueCountFrequency (%)
Ⅱ 3
42.9%
Ⅰ 2
28.6%
Ⅲ 2
28.6%
Space Separator
ValueCountFrequency (%)
14806
99.9%
　 19
 
0.1%
Modifier Letter
ValueCountFrequency (%)
ー 224
99.6%
ʻ 1
 
0.4%
Format
ValueCountFrequency (%)
‎ 2
66.7%
‌ 1
33.3%
Currency Symbol
ValueCountFrequency (%)
¢ 2
66.7%
$ 1
33.3%
Initial Punctuation
ValueCountFrequency (%)
“ 1
50.0%
« 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 104627
79.3%
Common 18536
 
14.0%
Han 3101
 
2.3%
Katakana 2147
 
1.6%
Hangul 1226
 
0.9%
Hiragana 1039
 
0.8%
Cyrillic 795
 
0.6%
Thai 323
 
0.2%
Devanagari 85
 
0.1%
Arabic 79
 
0.1%
Other values (4) 46
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
場 83
 
2.7%
劇 82
 
2.6%
版 81
 
2.6%
女 44
 
1.4%
之 37
 
1.2%
大 31
 
1.0%
人 28
 
0.9%
神 24
 
0.8%
天 23
 
0.7%
少 22
 
0.7%
Other values (931) 2646
85.3%
Hangul
ValueCountFrequency (%)
의 43
 
3.5%
이 23
 
1.9%
한 20
 
1.6%
마 20
 
1.6%
기 18
 
1.5%
사 17
 
1.4%
시 16
 
1.3%
아 16
 
1.3%
인 15
 
1.2%
엄 14
 
1.1%
Other values (356) 1024
83.5%
Latin
ValueCountFrequency (%)
e 12495
 
11.9%
a 7811
 
7.5%
o 7042
 
6.7%
n 6587
 
6.3%
r 6505
 
6.2%
i 6385
 
6.1%
t 5907
 
5.6%
s 4885
 
4.7%
h 4166
 
4.0%
l 4156
 
4.0%
Other values (111) 38688
37.0%
Katakana
ValueCountFrequency (%)
ン 206
 
9.6%
ラ 101
 
4.7%
ス 100
 
4.7%
ト 86
 
4.0%
ド 81
 
3.8%
ル 80
 
3.7%
イ 71
 
3.3%
ア 68
 
3.2%
リ 56
 
2.6%
ッ 55
 
2.6%
Other values (69) 1243
57.9%
Common
ValueCountFrequency (%)
14806
79.9%
: 863
 
4.7%
' 381
 
2.1%
2 330
 
1.8%
. 236
 
1.3%
- 232
 
1.3%
ー 224
 
1.2%
3 174
 
0.9%
1 150
 
0.8%
! 142
 
0.8%
Other values (68) 998
 
5.4%
Hiragana
ValueCountFrequency (%)
の 229
22.0%
と 49
 
4.7%
ん 42
 
4.0%
る 36
 
3.5%
た 35
 
3.4%
い 34
 
3.3%
か 34
 
3.3%
を 33
 
3.2%
し 31
 
3.0%
ら 28
 
2.7%
Other values (56) 488
47.0%
Cyrillic
ValueCountFrequency (%)
а 74
 
9.3%
о 73
 
9.2%
е 61
 
7.7%
р 55
 
6.9%
и 51
 
6.4%
н 49
 
6.2%
т 36
 
4.5%
к 32
 
4.0%
л 30
 
3.8%
в 26
 
3.3%
Other values (44) 308
38.7%
Thai
ValueCountFrequency (%)
า 25
 
7.7%
ก 23
 
7.1%
ร 21
 
6.5%
อ 17
 
5.3%
เ 15
 
4.6%
ั 14
 
4.3%
ม 14
 
4.3%
ต 13
 
4.0%
น 13
 
4.0%
ง 11
 
3.4%
Other values (38) 157
48.6%
Devanagari
ValueCountFrequency (%)
ा 13
 
15.3%
न 5
 
5.9%
क 5
 
5.9%
ल 5
 
5.9%
ध 4
 
4.7%
म 4
 
4.7%
ं 4
 
4.7%
र 4
 
4.7%
ग 4
 
4.7%
े 4
 
4.7%
Other values (21) 33
38.8%
Arabic
ValueCountFrequency (%)
ا 12
15.2%
ر 7
 
8.9%
م 6
 
7.6%
ل 6
 
7.6%
س 5
 
6.3%
ب 4
 
5.1%
ف 4
 
5.1%
ن 3
 
3.8%
ه 3
 
3.8%
و 3
 
3.8%
Other values (16) 26
32.9%
Greek
ValueCountFrequency (%)
ς 3
 
13.0%
ν 2
 
8.7%
ο 2
 
8.7%
Κ 1
 
4.3%
υ 1
 
4.3%
ό 1
 
4.3%
δ 1
 
4.3%
τ 1
 
4.3%
α 1
 
4.3%
η 1
 
4.3%
Other values (9) 9
39.1%
Telugu
ValueCountFrequency (%)
డ 2
18.2%
ీ 1
9.1%
జ 1
9.1%
ా 1
9.1%
ం 1
9.1%
బ 1
9.1%
ర 1
9.1%
ె 1
9.1%
్ 1
9.1%
ి 1
9.1%
Tamil
ValueCountFrequency (%)
க 1
25.0%
ை 1
25.0%
த 1
25.0%
ி 1
25.0%
Inherited
ValueCountFrequency (%)
゙ 5
62.5%
̀ 2
 
25.0%
‌ 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122335
92.7%
CJK 3101
 
2.3%
Katakana 2417
 
1.8%
Hangul 1226
 
0.9%
Hiragana 1044
 
0.8%
Cyrillic 795
 
0.6%
None 551
 
0.4%
Thai 323
 
0.2%
Devanagari 85
 
0.1%
Arabic 79
 
0.1%
Other values (10) 48
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
14806
 
12.1%
e 12495
 
10.2%
a 7811
 
6.4%
o 7042
 
5.8%
n 6587
 
5.4%
r 6505
 
5.3%
i 6385
 
5.2%
t 5907
 
4.8%
s 4885
 
4.0%
h 4166
 
3.4%
Other values (74) 45746
37.4%
Hiragana
ValueCountFrequency (%)
の 229
21.9%
と 49
 
4.7%
ん 42
 
4.0%
る 36
 
3.4%
た 35
 
3.4%
い 34
 
3.3%
か 34
 
3.3%
を 33
 
3.2%
し 31
 
3.0%
ら 28
 
2.7%
Other values (57) 493
47.2%
Katakana
ValueCountFrequency (%)
ー 224
 
9.3%
ン 206
 
8.5%
ラ 101
 
4.2%
ス 100
 
4.1%
ト 86
 
3.6%
ド 81
 
3.4%
ル 80
 
3.3%
イ 71
 
2.9%
ア 68
 
2.8%
リ 56
 
2.3%
Other values (71) 1344
55.6%
None
ValueCountFrequency (%)
é 92
 
16.7%
è 30
 
5.4%
～ 26
 
4.7%
ó 25
 
4.5%
： 21
 
3.8%
í 20
 
3.6%
　 19
 
3.4%
á 17
 
3.1%
à 16
 
2.9%
！ 14
 
2.5%
Other values (107) 271
49.2%
CJK
ValueCountFrequency (%)
場 83
 
2.7%
劇 82
 
2.6%
版 81
 
2.6%
女 44
 
1.4%
之 37
 
1.2%
大 31
 
1.0%
人 28
 
0.9%
神 24
 
0.8%
天 23
 
0.7%
少 22
 
0.7%
Other values (931) 2646
85.3%
Cyrillic
ValueCountFrequency (%)
а 74
 
9.3%
о 73
 
9.2%
е 61
 
7.7%
р 55
 
6.9%
и 51
 
6.4%
н 49
 
6.2%
т 36
 
4.5%
к 32
 
4.0%
л 30
 
3.8%
в 26
 
3.3%
Other values (44) 308
38.7%
Hangul
ValueCountFrequency (%)
의 43
 
3.5%
이 23
 
1.9%
한 20
 
1.6%
마 20
 
1.6%
기 18
 
1.5%
사 17
 
1.4%
시 16
 
1.3%
아 16
 
1.3%
인 15
 
1.2%
엄 14
 
1.1%
Other values (356) 1024
83.5%
Thai
ValueCountFrequency (%)
า 25
 
7.7%
ก 23
 
7.1%
ร 21
 
6.5%
อ 17
 
5.3%
เ 15
 
4.6%
ั 14
 
4.3%
ม 14
 
4.3%
ต 13
 
4.0%
น 13
 
4.0%
ง 11
 
3.4%
Other values (38) 157
48.6%
Devanagari
ValueCountFrequency (%)
ा 13
 
15.3%
न 5
 
5.9%
क 5
 
5.9%
ल 5
 
5.9%
ध 4
 
4.7%
म 4
 
4.7%
ं 4
 
4.7%
र 4
 
4.7%
ग 4
 
4.7%
े 4
 
4.7%
Other values (21) 33
38.8%
Arabic
ValueCountFrequency (%)
ا 12
15.2%
ر 7
 
8.9%
م 6
 
7.6%
ل 6
 
7.6%
س 5
 
6.3%
ب 4
 
5.1%
ف 4
 
5.1%
ن 3
 
3.8%
ه 3
 
3.8%
و 3
 
3.8%
Other values (16) 26
32.9%
Punctuation
ValueCountFrequency (%)
’ 6
42.9%
‎ 2
 
14.3%
― 2
 
14.3%
” 1
 
7.1%
“ 1
 
7.1%
‌ 1
 
7.1%
– 1
 
7.1%
Misc Symbols
ValueCountFrequency (%)
☆ 4
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 3
37.5%
Ⅰ 2
25.0%
Ⅲ 2
25.0%
⅓ 1
 
12.5%
Latin Ext Additional
ValueCountFrequency (%)
ọ 2
100.0%
Diacriticals
ValueCountFrequency (%)
̀ 2
100.0%
Telugu
ValueCountFrequency (%)
డ 2
18.2%
ీ 1
9.1%
జ 1
9.1%
ా 1
9.1%
ం 1
9.1%
బ 1
9.1%
ర 1
9.1%
ె 1
9.1%
్ 1
9.1%
ి 1
9.1%
Geometric Shapes
ValueCountFrequency (%)
△ 1
100.0%
Math Operators
ValueCountFrequency (%)
∞ 1
100.0%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%
Tamil
ValueCountFrequency (%)
க 1
25.0%
ை 1
25.0%
த 1
25.0%
ி 1
25.0%

Overview
Text

Distinct8539
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
2023-11-01T18:58:55.008020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length579
Median length371
Mean length149.43616
Min length0

Characters and Unicode

Total characters1277978
Distinct characters46
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8536 ?
Unique (%)99.8%

Sample

1st rowskilled thief corporate espionage subconscious target chance regain old life payment task considered impossible inception implantation another person idea target subconscious
2nd rowalso known black widow part ledger dangerous conspiracy tie past force stop nothing bring must deal history spy broken relationship left wake long avenger
3rd rowcentury matrix tell story computer hacker join group underground insurgent fighting vast powerful computer rule earth
4th rowuninhabitable earth outcome civil war hinge brain elite soldier create robot mercenary
5th rowqueen poppy branch make surprising discovery — troll world beyond distinct difference create big clash various tribe mysterious threat put troll across land danger poppy branch band friend must embark epic quest create harmony among troll unite certain doom
ValueCountFrequency (%)
life 1648
 
0.9%
find 1355
 
0.7%
one 1141
 
0.6%
new 1135
 
0.6%
young 1124
 
0.6%
world 1079
 
0.6%
friend 958
 
0.5%
family 926
 
0.5%
must 896
 
0.5%
two 835
 
0.4%
Other values (12502) 178468
94.1%
2023-11-01T18:58:56.071901image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
181014
14.2%
e 139143
 
10.9%
r 85547
 
6.7%
a 83457
 
6.5%
t 81496
 
6.4%
i 79836
 
6.2%
n 78850
 
6.2%
o 73680
 
5.8%
l 58945
 
4.6%
s 58884
 
4.6%
Other values (36) 357126
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1095343
85.7%
Space Separator 181014
 
14.2%
Dash Punctuation 746
 
0.1%
Final Punctuation 652
 
0.1%
Initial Punctuation 118
 
< 0.1%
Other Punctuation 85
 
< 0.1%
Other Symbol 11
 
< 0.1%
Nonspacing Mark 3
 
< 0.1%
Modifier Symbol 2
 
< 0.1%
Format 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 139143
12.7%
r 85547
 
7.8%
a 83457
 
7.6%
t 81496
 
7.4%
i 79836
 
7.3%
n 78850
 
7.2%
o 73680
 
6.7%
l 58945
 
5.4%
s 58884
 
5.4%
c 42156
 
3.8%
Other values (16) 313349
28.6%
Final Punctuation
ValueCountFrequency (%)
’ 563
86.3%
” 88
 
13.5%
» 1
 
0.2%
Initial Punctuation
ValueCountFrequency (%)
“ 86
72.9%
‘ 31
 
26.3%
« 1
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
— 507
68.0%
– 239
32.0%
Other Punctuation
ValueCountFrequency (%)
… 82
96.5%
• 3
 
3.5%
Other Symbol
ValueCountFrequency (%)
™ 9
81.8%
® 2
 
18.2%
Nonspacing Mark
ValueCountFrequency (%)
́ 2
66.7%
̈ 1
33.3%
Format
ValueCountFrequency (%)
⁠ 1
50.0%
‎ 1
50.0%
Space Separator
ValueCountFrequency (%)
181014
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%
Currency Symbol
ValueCountFrequency (%)
£ 1
100.0%
Other Number
ValueCountFrequency (%)
¹ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1095343
85.7%
Common 182632
 
14.3%
Inherited 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 139143
12.7%
r 85547
 
7.8%
a 83457
 
7.6%
t 81496
 
7.4%
i 79836
 
7.3%
n 78850
 
7.2%
o 73680
 
6.7%
l 58945
 
5.4%
s 58884
 
5.4%
c 42156
 
3.8%
Other values (16) 313349
28.6%
Common
ValueCountFrequency (%)
181014
99.1%
’ 563
 
0.3%
— 507
 
0.3%
– 239
 
0.1%
” 88
 
< 0.1%
“ 86
 
< 0.1%
… 82
 
< 0.1%
‘ 31
 
< 0.1%
™ 9
 
< 0.1%
• 3
 
< 0.1%
Other values (8) 10
 
< 0.1%
Inherited
ValueCountFrequency (%)
́ 2
66.7%
̈ 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1276357
99.9%
Punctuation 1601
 
0.1%
Letterlike Symbols 9
 
< 0.1%
None 8
 
< 0.1%
Diacriticals 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
181014
14.2%
e 139143
 
10.9%
r 85547
 
6.7%
a 83457
 
6.5%
t 81496
 
6.4%
i 79836
 
6.3%
n 78850
 
6.2%
o 73680
 
5.8%
l 58945
 
4.6%
s 58884
 
4.6%
Other values (17) 355505
27.9%
Punctuation
ValueCountFrequency (%)
’ 563
35.2%
— 507
31.7%
– 239
14.9%
” 88
 
5.5%
“ 86
 
5.4%
… 82
 
5.1%
‘ 31
 
1.9%
• 3
 
0.2%
⁠ 1
 
0.1%
‎ 1
 
0.1%
Letterlike Symbols
ValueCountFrequency (%)
™ 9
100.0%
Diacriticals
ValueCountFrequency (%)
́ 2
66.7%
̈ 1
33.3%
None
ValueCountFrequency (%)
´ 2
25.0%
® 2
25.0%
« 1
12.5%
£ 1
12.5%
¹ 1
12.5%
» 1
12.5%

Popularity
Real number (ℝ)

Distinct6966
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0878946
Minimum2.5687115
Maximum4.4012163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2023-11-01T18:58:56.327464image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2.5687115
5-th percentile2.6002831
Q12.7389673
median2.9672042
Q33.3278109
95-th percentile4.0105634
Maximum4.4012163
Range1.8325048
Interquartile range (IQR)0.58884361

Descriptive statistics

Standard deviation0.43646712
Coefficient of variation (CV)0.1413478
Kurtosis0.23845823
Mean3.0878946
Median Absolute Deviation (MAD)0.26624574
Skewness1.0027189
Sum26407.675
Variance0.19050355
MonotonicityDecreasing
2023-11-01T18:58:56.583422image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.587538446 6
 
0.1%
2.740775506 6
 
0.1%
2.781672341 6
 
0.1%
2.690497042 5
 
0.1%
2.66924007 4
 
< 0.1%
2.892757798 4
 
< 0.1%
2.64808786 4
 
< 0.1%
2.615203651 4
 
< 0.1%
2.647379745 4
 
< 0.1%
2.719715233 4
 
< 0.1%
Other values (6956) 8505
99.5%
ValueCountFrequency (%)
2.568711502 3
< 0.1%
2.568788134 1
 
< 0.1%
2.568864759 3
< 0.1%
2.568941379 2
< 0.1%
2.5690946 2
< 0.1%
2.569171202 2
< 0.1%
2.569247798 1
 
< 0.1%
2.569324388 2
< 0.1%
2.569477551 1
 
< 0.1%
2.569783807 2
< 0.1%
ValueCountFrequency (%)
4.401216329 1
< 0.1%
4.399596379 1
< 0.1%
4.393979909 1
< 0.1%
4.393819327 1
< 0.1%
4.393436296 1
< 0.1%
4.390862483 1
< 0.1%
4.385906598 1
< 0.1%
4.384972292 1
< 0.1%
4.384934902 1
< 0.1%
4.383575435 1
< 0.1%

ReleaseDate
Date

Distinct5445
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
Minimum1920-02-27 00:00:00
Maximum2023-10-26 00:00:00
2023-11-01T18:58:56.825280image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:57.031315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Title
Text

Distinct8264
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
2023-11-01T18:58:57.485900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length104
Median length70
Mean length16.821562
Min length1

Characters and Unicode

Total characters143858
Distinct characters127
Distinct categories17 ?
Distinct scripts5 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8002 ?
Unique (%)93.6%

Sample

1st rowInception
2nd rowBlack Widow
3rd rowThe Matrix
4th rowJUNG_E
5th rowTrolls World Tour
ValueCountFrequency (%)
the 2893
 
11.3%
of 869
 
3.4%
a 358
 
1.4%
and 285
 
1.1%
in 259
 
1.0%
2 252
 
1.0%
to 190
 
0.7%
174
 
0.7%
movie 147
 
0.6%
love 109
 
0.4%
Other values (6917) 20090
78.4%
2023-11-01T18:58:58.206781image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17073
 
11.9%
e 14842
 
10.3%
a 8822
 
6.1%
o 8648
 
6.0%
n 7771
 
5.4%
r 7587
 
5.3%
i 7505
 
5.2%
t 7162
 
5.0%
s 5581
 
3.9%
h 5379
 
3.7%
Other values (117) 53488
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100686
70.0%
Uppercase Letter 22376
 
15.6%
Space Separator 17075
 
11.9%
Other Punctuation 2334
 
1.6%
Decimal Number 1061
 
0.7%
Dash Punctuation 258
 
0.2%
Close Punctuation 18
 
< 0.1%
Open Punctuation 18
 
< 0.1%
Other Number 10
 
< 0.1%
Other Letter 8
 
< 0.1%
Other values (7) 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14842
14.7%
a 8822
 
8.8%
o 8648
 
8.6%
n 7771
 
7.7%
r 7587
 
7.5%
i 7505
 
7.5%
t 7162
 
7.1%
s 5581
 
5.5%
h 5379
 
5.3%
l 4721
 
4.7%
Other values (30) 22668
22.5%
Uppercase Letter
ValueCountFrequency (%)
T 2977
 
13.3%
S 1958
 
8.8%
M 1506
 
6.7%
B 1442
 
6.4%
A 1279
 
5.7%
C 1271
 
5.7%
D 1258
 
5.6%
P 1063
 
4.8%
L 1055
 
4.7%
H 949
 
4.2%
Other values (20) 7618
34.0%
Other Punctuation
ValueCountFrequency (%)
: 1182
50.6%
' 416
 
17.8%
. 241
 
10.3%
, 151
 
6.5%
! 138
 
5.9%
& 124
 
5.3%
/ 33
 
1.4%
? 28
 
1.2%
* 7
 
0.3%
¡ 3
 
0.1%
Other values (7) 11
 
0.5%
Decimal Number
ValueCountFrequency (%)
2 337
31.8%
3 175
16.5%
1 153
14.4%
0 127
 
12.0%
4 73
 
6.9%
9 49
 
4.6%
5 44
 
4.1%
7 39
 
3.7%
8 33
 
3.1%
6 31
 
2.9%
Other Letter
ValueCountFrequency (%)
爆 1
12.5%
撃 1
12.5%
衝 1
12.5%
の 1
12.5%
乳 1
12.5%
瞳 1
12.5%
中 1
12.5%
田 1
12.5%
Other Number
ValueCountFrequency (%)
½ 5
50.0%
³ 2
 
20.0%
⅓ 1
 
10.0%
² 1
 
10.0%
⁴ 1
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 247
95.7%
– 8
 
3.1%
— 3
 
1.2%
Space Separator
ValueCountFrequency (%)
17073
> 99.9%
  2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 13
72.2%
] 5
 
27.8%
Open Punctuation
ValueCountFrequency (%)
( 13
72.2%
[ 5
 
27.8%
Currency Symbol
ValueCountFrequency (%)
¢ 2
66.7%
$ 1
33.3%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 3
100.0%
Nonspacing Mark
ValueCountFrequency (%)
̀ 2
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%
Format
ValueCountFrequency (%)
­ 1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 123062
85.5%
Common 20786
 
14.4%
Han 7
 
< 0.1%
Inherited 2
 
< 0.1%
Hiragana 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14842
 
12.1%
a 8822
 
7.2%
o 8648
 
7.0%
n 7771
 
6.3%
r 7587
 
6.2%
i 7505
 
6.1%
t 7162
 
5.8%
s 5581
 
4.5%
h 5379
 
4.4%
l 4721
 
3.8%
Other values (60) 45044
36.6%
Common
ValueCountFrequency (%)
17073
82.1%
: 1182
 
5.7%
' 416
 
2.0%
2 337
 
1.6%
- 247
 
1.2%
. 241
 
1.2%
3 175
 
0.8%
1 153
 
0.7%
, 151
 
0.7%
! 138
 
0.7%
Other values (38) 673
 
3.2%
Han
ValueCountFrequency (%)
爆 1
14.3%
撃 1
14.3%
衝 1
14.3%
乳 1
14.3%
瞳 1
14.3%
中 1
14.3%
田 1
14.3%
Inherited
ValueCountFrequency (%)
̀ 2
100.0%
Hiragana
ValueCountFrequency (%)
の 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143739
99.9%
None 89
 
0.1%
Punctuation 16
 
< 0.1%
CJK 7
 
< 0.1%
Latin Ext Additional 2
 
< 0.1%
Diacriticals 2
 
< 0.1%
Modifier Letters 1
 
< 0.1%
Number Forms 1
 
< 0.1%
Hiragana 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17073
 
11.9%
e 14842
 
10.3%
a 8822
 
6.1%
o 8648
 
6.0%
n 7771
 
5.4%
r 7587
 
5.3%
i 7505
 
5.2%
t 7162
 
5.0%
s 5581
 
3.9%
h 5379
 
3.7%
Other values (75) 53369
37.1%
None
ValueCountFrequency (%)
é 43
48.3%
½ 5
 
5.6%
á 4
 
4.5%
í 4
 
4.5%
è 3
 
3.4%
ó 3
 
3.4%
¡ 3
 
3.4%
à 3
 
3.4%
¿ 2
 
2.2%
¢ 2
 
2.2%
Other values (16) 17
 
19.1%
Punctuation
ValueCountFrequency (%)
– 8
50.0%
— 3
 
18.8%
’ 3
 
18.8%
  2
 
12.5%
Latin Ext Additional
ValueCountFrequency (%)
ọ 2
100.0%
Diacriticals
ValueCountFrequency (%)
̀ 2
100.0%
CJK
ValueCountFrequency (%)
爆 1
14.3%
撃 1
14.3%
衝 1
14.3%
乳 1
14.3%
瞳 1
14.3%
中 1
14.3%
田 1
14.3%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%
Number Forms
ValueCountFrequency (%)
⅓ 1
100.0%
Hiragana
ValueCountFrequency (%)
の 1
100.0%

VoteAverage
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5354654
Minimum4.4
Maximum8.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2023-11-01T18:58:58.440269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum4.4
5-th percentile5.2
Q16
median6.6
Q37.1
95-th percentile7.8
Maximum8.6
Range4.2
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.80047008
Coefficient of variation (CV)0.12248096
Kurtosis-0.33411624
Mean6.5354654
Median Absolute Deviation (MAD)0.6
Skewness-0.15746386
Sum55891.3
Variance0.64075235
MonotonicityNot monotonic
2023-11-01T18:58:58.643845image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
6.5 425
 
5.0%
6.3 409
 
4.8%
6.6 402
 
4.7%
6.8 398
 
4.7%
7 394
 
4.6%
6.2 385
 
4.5%
6.7 385
 
4.5%
6.4 381
 
4.5%
6.9 376
 
4.4%
6.1 374
 
4.4%
Other values (33) 4623
54.1%
ValueCountFrequency (%)
4.4 22
 
0.3%
4.5 38
 
0.4%
4.6 39
 
0.5%
4.7 46
 
0.5%
4.8 48
 
0.6%
4.9 62
0.7%
5 79
0.9%
5.1 84
1.0%
5.2 121
1.4%
5.3 139
1.6%
ValueCountFrequency (%)
8.6 2
 
< 0.1%
8.5 10
 
0.1%
8.4 26
 
0.3%
8.3 39
 
0.5%
8.2 55
 
0.6%
8.1 61
 
0.7%
8 87
1.0%
7.9 111
1.3%
7.8 129
1.5%
7.7 153
1.8%

VoteCount
Real number (ℝ)

HIGH CORRELATION 

Distinct3261
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2986884
Minimum0
Maximum10.452418
Zeros27
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2023-11-01T18:58:58.871879image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2580965
Q15.370638
median6.4281053
Q37.4312997
95-th percentile8.7395204
Maximum10.452418
Range10.452418
Interquartile range (IQR)2.0606616

Descriptive statistics

Standard deviation1.6593754
Coefficient of variation (CV)0.26344777
Kurtosis0.95554062
Mean6.2986884
Median Absolute Deviation (MAD)1.0254422
Skewness-0.70407864
Sum53866.383
Variance2.7535267
MonotonicityNot monotonic
2023-11-01T18:58:59.104523image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6931471806 32
 
0.4%
1.609437912 30
 
0.4%
1.386294361 28
 
0.3%
0 27
 
0.3%
1.945910149 26
 
0.3%
2.079441542 25
 
0.3%
2.302585093 25
 
0.3%
1.098612289 21
 
0.2%
3.583518938 19
 
0.2%
4.700480366 19
 
0.2%
Other values (3251) 8300
97.1%
ValueCountFrequency (%)
0 27
0.3%
0.6931471806 32
0.4%
1.098612289 21
0.2%
1.386294361 28
0.3%
1.609437912 30
0.4%
1.791759469 17
0.2%
1.945910149 26
0.3%
2.079441542 25
0.3%
2.197224577 18
0.2%
2.302585093 25
0.3%
ValueCountFrequency (%)
10.45241788 1
< 0.1%
10.19320505 1
< 0.1%
10.08434971 1
< 0.1%
10.01721794 1
< 0.1%
9.980448594 1
< 0.1%
9.957833682 1
< 0.1%
9.957549511 1
< 0.1%
9.951896692 1
< 0.1%
9.951277216 1
< 0.1%
9.937888979 1
< 0.1%

Budget
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct639
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5715398
Minimum0
Maximum19.519293
Zeros3604
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2023-11-01T18:58:59.328841image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.045134
Q317.034386
95-th percentile18.289713
Maximum19.519293
Range19.519293
Interquartile range (IQR)17.034386

Descriptive statistics

Standard deviation8.2632118
Coefficient of variation (CV)0.8633106
Kurtosis-1.8715781
Mean9.5715398
Median Absolute Deviation (MAD)3.0878647
Skewness-0.26021247
Sum81855.808
Variance68.280669
MonotonicityNot monotonic
2023-11-01T18:58:59.558415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3604
42.1%
16.81124283 193
 
2.3%
17.21670794 173
 
2.0%
17.03438638 167
 
2.0%
16.11809565 159
 
1.9%
16.52356076 149
 
1.7%
17.50439001 143
 
1.7%
15.42494847 141
 
1.6%
17.72753356 128
 
1.5%
17.37085862 124
 
1.4%
Other values (629) 3571
41.8%
ValueCountFrequency (%)
0 3604
42.1%
1.386294361 1
 
< 0.1%
1.609437912 1
 
< 0.1%
1.791759469 1
 
< 0.1%
1.945910149 1
 
< 0.1%
2.995732274 1
 
< 0.1%
3.258096538 1
 
< 0.1%
3.555348061 1
 
< 0.1%
4.418840608 1
 
< 0.1%
4.48863637 1
 
< 0.1%
ValueCountFrequency (%)
19.51929303 1
 
< 0.1%
19.33697148 6
 
0.1%
19.31676877 2
 
< 0.1%
19.25358987 1
 
< 0.1%
19.23161096 3
 
< 0.1%
19.18614859 1
 
< 0.1%
19.15784481 1
 
< 0.1%
19.13852054 1
 
< 0.1%
19.11382792 32
0.4%
19.08851012 1
 
< 0.1%

TagLine
Text

MISSING 

Distinct6728
Distinct (%)99.4%
Missing1781
Missing (%)20.8%
Memory size391.7 KiB
2023-11-01T18:59:00.009121image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length206
Median length142
Mean length40.110914
Min length3

Characters and Unicode

Total characters271591
Distinct characters99
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6688 ?
Unique (%)98.8%

Sample

1st rowYour mind is the scene of the crime.
2nd rowHer world. Her secrets. Her legacy.
3rd rowWelcome to the Real World.
4th rowAI Combat Warrior Will be Unleashed.
5th rowHappiest. Movie. Ever.
ValueCountFrequency (%)
the 3146
 
6.3%
a 1814
 
3.6%
to 1094
 
2.2%
of 1018
 
2.0%
is 1018
 
2.0%
you 893
 
1.8%
in 708
 
1.4%
and 517
 
1.0%
for 513
 
1.0%
one 458
 
0.9%
Other values (5958) 38869
77.7%
2023-11-01T18:59:01.964716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43285
15.9%
e 28672
 
10.6%
t 16834
 
6.2%
o 16785
 
6.2%
a 14504
 
5.3%
n 13712
 
5.0%
i 13271
 
4.9%
r 13223
 
4.9%
s 12809
 
4.7%
h 10845
 
4.0%
Other values (89) 87651
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 199889
73.6%
Space Separator 43291
 
15.9%
Uppercase Letter 14781
 
5.4%
Other Punctuation 12453
 
4.6%
Decimal Number 810
 
0.3%
Dash Punctuation 237
 
0.1%
Final Punctuation 96
 
< 0.1%
Open Punctuation 10
 
< 0.1%
Close Punctuation 10
 
< 0.1%
Currency Symbol 8
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28672
14.3%
t 16834
 
8.4%
o 16785
 
8.4%
a 14504
 
7.3%
n 13712
 
6.9%
i 13271
 
6.6%
r 13223
 
6.6%
s 12809
 
6.4%
h 10845
 
5.4%
l 8620
 
4.3%
Other values (24) 50614
25.3%
Uppercase Letter
ValueCountFrequency (%)
T 2378
16.1%
A 1310
 
8.9%
S 1070
 
7.2%
W 888
 
6.0%
H 871
 
5.9%
I 864
 
5.8%
B 735
 
5.0%
N 674
 
4.6%
F 665
 
4.5%
E 645
 
4.4%
Other values (16) 4681
31.7%
Other Punctuation
ValueCountFrequency (%)
. 8344
67.0%
' 1589
 
12.8%
, 1115
 
9.0%
! 787
 
6.3%
? 385
 
3.1%
… 89
 
0.7%
" 55
 
0.4%
: 34
 
0.3%
% 16
 
0.1%
* 13
 
0.1%
Other values (4) 26
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 240
29.6%
1 171
21.1%
2 96
 
11.9%
9 61
 
7.5%
3 55
 
6.8%
5 44
 
5.4%
6 39
 
4.8%
7 38
 
4.7%
8 34
 
4.2%
4 32
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 227
95.8%
— 6
 
2.5%
– 4
 
1.7%
Space Separator
ValueCountFrequency (%)
43285
> 99.9%
  6
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
’ 94
97.9%
” 2
 
2.1%
Initial Punctuation
ValueCountFrequency (%)
“ 2
66.7%
‘ 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 8
100.0%
Modifier Letter
ValueCountFrequency (%)
ʻ 1
100.0%
Other Number
ValueCountFrequency (%)
½ 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 214670
79.0%
Common 56921
 
21.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28672
13.4%
t 16834
 
7.8%
o 16785
 
7.8%
a 14504
 
6.8%
n 13712
 
6.4%
i 13271
 
6.2%
r 13223
 
6.2%
s 12809
 
6.0%
h 10845
 
5.1%
l 8620
 
4.0%
Other values (50) 65395
30.5%
Common
ValueCountFrequency (%)
43285
76.0%
. 8344
 
14.7%
' 1589
 
2.8%
, 1115
 
2.0%
! 787
 
1.4%
? 385
 
0.7%
0 240
 
0.4%
- 227
 
0.4%
1 171
 
0.3%
2 96
 
0.2%
Other values (29) 682
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271371
99.9%
Punctuation 198
 
0.1%
None 21
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43285
16.0%
e 28672
 
10.6%
t 16834
 
6.2%
o 16785
 
6.2%
a 14504
 
5.3%
n 13712
 
5.1%
i 13271
 
4.9%
r 13223
 
4.9%
s 12809
 
4.7%
h 10845
 
4.0%
Other values (71) 87431
32.2%
Punctuation
ValueCountFrequency (%)
’ 94
47.5%
… 89
44.9%
— 6
 
3.0%
– 4
 
2.0%
“ 2
 
1.0%
” 2
 
1.0%
‘ 1
 
0.5%
None
ValueCountFrequency (%)
  6
28.6%
é 4
19.0%
ñ 2
 
9.5%
ü 2
 
9.5%
á 2
 
9.5%
ō 1
 
4.8%
í 1
 
4.8%
½ 1
 
4.8%
ù 1
 
4.8%
ê 1
 
4.8%
Modifier Letters
ValueCountFrequency (%)
ʻ 1
100.0%

RunTime
Real number (ℝ)

Distinct97
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.63599
Minimum55
Maximum151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2023-11-01T18:59:02.215257image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile79
Q192
median102
Q3115
95-th percentile134
Maximum151
Range96
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.675884
Coefficient of variation (CV)0.16090823
Kurtosis-0.051048792
Mean103.63599
Median Absolute Deviation (MAD)11
Skewness0.32281219
Sum886295
Variance278.08512
MonotonicityNot monotonic
2023-11-01T18:59:02.444328image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 269
 
3.1%
90 262
 
3.1%
100 246
 
2.9%
93 240
 
2.8%
105 230
 
2.7%
97 229
 
2.7%
98 226
 
2.6%
94 217
 
2.5%
101 214
 
2.5%
96 212
 
2.5%
Other values (87) 6207
72.6%
ValueCountFrequency (%)
55 3
 
< 0.1%
56 4
 
< 0.1%
57 3
 
< 0.1%
58 4
 
< 0.1%
59 7
0.1%
60 15
0.2%
61 8
0.1%
62 6
 
0.1%
63 7
0.1%
64 10
0.1%
ValueCountFrequency (%)
151 12
0.1%
150 12
0.1%
149 13
0.2%
148 10
0.1%
147 18
0.2%
146 15
0.2%
145 21
0.2%
144 13
0.2%
143 24
0.3%
142 14
0.2%

Revenue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5092
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.555716
Minimum0
Maximum21.449956
Zeros3247
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2023-11-01T18:59:02.683418image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.57572
Q317.822244
95-th percentile19.401506
Maximum21.449956
Range21.449956
Interquartile range (IQR)17.822244

Descriptive statistics

Standard deviation8.4310991
Coefficient of variation (CV)0.79872354
Kurtosis-1.7385716
Mean10.555716
Median Absolute Deviation (MAD)3.3579764
Skewness-0.38812462
Sum90272.486
Variance71.083432
MonotonicityNot monotonic
2023-11-01T18:59:02.910047image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3247
38.0%
16.21340583 11
 
0.1%
14.50865774 10
 
0.1%
16.30041721 10
 
0.1%
17.03438638 8
 
0.1%
16.11809565 8
 
0.1%
15.76142071 8
 
0.1%
17.21670794 8
 
0.1%
15.42494847 7
 
0.1%
15.8949521 6
 
0.1%
Other values (5082) 5229
61.1%
ValueCountFrequency (%)
0 3247
38.0%
1.098612289 1
 
< 0.1%
1.945910149 1
 
< 0.1%
2.302585093 1
 
< 0.1%
3.36729583 1
 
< 0.1%
3.761200116 1
 
< 0.1%
4.543294782 1
 
< 0.1%
4.836281907 1
 
< 0.1%
5.303304908 1
 
< 0.1%
5.733341277 1
 
< 0.1%
ValueCountFrequency (%)
21.44995592 1
< 0.1%
21.23700967 1
< 0.1%
21.1389066 1
< 0.1%
21.02331567 1
< 0.1%
20.99364886 1
< 0.1%
20.95921976 1
< 0.1%
20.94063705 1
< 0.1%
20.93515034 1
< 0.1%
20.91848487 1
< 0.1%
20.86886373 1
< 0.1%

Genres
Text

Distinct2086
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
2023-11-01T18:59:03.143208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length84
Median length60
Mean length21.514032
Min length0

Characters and Unicode

Total characters183988
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1309 ?
Unique (%)15.3%

Sample

1st rowAction, ScienceFiction, Adventure
2nd rowAction, Adventure, ScienceFiction
3rd rowAction, ScienceFiction
4th rowScienceFiction
5th rowFamily, Animation, Comedy, Fantasy, Adventure, Music
ValueCountFrequency (%)
drama 3327
14.6%
comedy 2665
11.7%
thriller 2403
10.6%
action 2361
10.4%
adventure 1589
 
7.0%
romance 1385
 
6.1%
horror 1363
 
6.0%
crime 1198
 
5.3%
fantasy 1121
 
4.9%
family 1118
 
4.9%
Other values (10) 4209
18.5%
2023-11-01T18:59:03.633068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 16620
 
9.0%
14370
 
7.8%
e 14302
 
7.8%
, 14040
 
7.6%
i 12988
 
7.1%
a 12745
 
6.9%
o 11803
 
6.4%
n 10836
 
5.9%
m 10798
 
5.9%
t 8514
 
4.6%
Other values (18) 56972
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 131763
71.6%
Uppercase Letter 23815
 
12.9%
Space Separator 14370
 
7.8%
Other Punctuation 14040
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 16620
12.6%
e 14302
10.9%
i 12988
9.9%
a 12745
9.7%
o 11803
9.0%
n 10836
8.2%
m 10798
8.2%
t 8514
6.5%
c 7227
 
5.5%
y 6940
 
5.3%
Other values (6) 18990
14.4%
Uppercase Letter
ValueCountFrequency (%)
A 4961
20.8%
C 3863
16.2%
D 3421
14.4%
F 3301
13.9%
T 2572
10.8%
H 1693
 
7.1%
R 1385
 
5.8%
M 1176
 
4.9%
S 1062
 
4.5%
W 381
 
1.6%
Space Separator
ValueCountFrequency (%)
14370
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14040
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 155578
84.6%
Common 28410
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 16620
 
10.7%
e 14302
 
9.2%
i 12988
 
8.3%
a 12745
 
8.2%
o 11803
 
7.6%
n 10836
 
7.0%
m 10798
 
6.9%
t 8514
 
5.5%
c 7227
 
4.6%
y 6940
 
4.5%
Other values (16) 42805
27.5%
Common
ValueCountFrequency (%)
14370
50.6%
, 14040
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 183988
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 16620
 
9.0%
14370
 
7.8%
e 14302
 
7.8%
, 14040
 
7.6%
i 12988
 
7.1%
a 12745
 
6.9%
o 11803
 
6.4%
n 10836
 
5.9%
m 10798
 
5.9%
t 8514
 
4.6%
Other values (18) 56972
31.0%

North America
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
1
6152 
0
2400 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8552
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 6152
71.9%
0 2400
 
28.1%

Length

2023-11-01T18:59:03.830736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T18:59:04.018944image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
1 6152
71.9%
0 2400
 
28.1%

Most occurring characters

ValueCountFrequency (%)
1 6152
71.9%
0 2400
 
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8552
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6152
71.9%
0 2400
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6152
71.9%
0 2400
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6152
71.9%
0 2400
 
28.1%

Europe
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
0
6137 
1
2415 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8552
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6137
71.8%
1 2415
 
28.2%

Length

2023-11-01T18:59:04.172496image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T18:59:04.350614image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 6137
71.8%
1 2415
 
28.2%

Most occurring characters

ValueCountFrequency (%)
0 6137
71.8%
1 2415
 
28.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8552
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6137
71.8%
1 2415
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6137
71.8%
1 2415
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6137
71.8%
1 2415
 
28.2%

Asia
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
0
7132 
1
1420 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8552
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 7132
83.4%
1 1420
 
16.6%

Length

2023-11-01T18:59:04.499323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T18:59:04.684855image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 7132
83.4%
1 1420
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 7132
83.4%
1 1420
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8552
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7132
83.4%
1 1420
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 8552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7132
83.4%
1 1420
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7132
83.4%
1 1420
 
16.6%

Oceania
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
0
8350 
1
 
202

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8552
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8350
97.6%
1 202
 
2.4%

Length

2023-11-01T18:59:04.842497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T18:59:05.033176image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8350
97.6%
1 202
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 8350
97.6%
1 202
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8552
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8350
97.6%
1 202
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8350
97.6%
1 202
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8350
97.6%
1 202
 
2.4%

South America
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
0
8448 
1
 
104

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8552
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8448
98.8%
1 104
 
1.2%

Length

2023-11-01T18:59:05.190047image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T18:59:05.367077image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8448
98.8%
1 104
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 8448
98.8%
1 104
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8552
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8448
98.8%
1 104
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8448
98.8%
1 104
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8448
98.8%
1 104
 
1.2%

Africa
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size391.7 KiB
0
8485 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8552
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8485
99.2%
1 67
 
0.8%

Length

2023-11-01T18:59:05.520334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-01T18:59:05.702057image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8485
99.2%
1 67
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 8485
99.2%
1 67
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8552
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8485
99.2%
1 67
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 8552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8485
99.2%
1 67
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8485
99.2%
1 67
 
0.8%

Year
Real number (ℝ)

Distinct100
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.3337
Minimum1920
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size358.3 KiB
2023-11-01T18:59:05.902271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1920
5-th percentile1974
Q11999
median2011
Q32018
95-th percentile2022
Maximum2023
Range103
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.026195
Coefficient of variation (CV)0.0079878011
Kurtosis2.7768258
Mean2006.3337
Median Absolute Deviation (MAD)9
Skewness-1.5426722
Sum17158166
Variance256.83891
MonotonicityNot monotonic
2023-11-01T18:59:06.148017image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022 548
 
6.4%
2018 408
 
4.8%
2023 395
 
4.6%
2019 393
 
4.6%
2021 391
 
4.6%
2017 358
 
4.2%
2020 352
 
4.1%
2016 319
 
3.7%
2015 280
 
3.3%
2014 278
 
3.3%
Other values (90) 4830
56.5%
ValueCountFrequency (%)
1920 1
 
< 0.1%
1921 1
 
< 0.1%
1922 1
 
< 0.1%
1925 2
 
< 0.1%
1927 2
 
< 0.1%
1928 1
 
< 0.1%
1930 1
 
< 0.1%
1931 5
0.1%
1932 3
< 0.1%
1933 3
< 0.1%
ValueCountFrequency (%)
2023 395
4.6%
2022 548
6.4%
2021 391
4.6%
2020 352
4.1%
2019 393
4.6%
2018 408
4.8%
2017 358
4.2%
2016 319
3.7%
2015 280
3.3%
2014 278
3.3%

weighted_average
Real number (ℝ)

HIGH CORRELATION 

Distinct7849
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5498904
Minimum5.4038125
Maximum7.6334925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size391.7 KiB
2023-11-01T18:59:06.400137image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum5.4038125
5-th percentile5.974818
Q16.3132446
median6.5354654
Q36.7889355
95-th percentile7.1370983
Maximum7.6334925
Range2.22968
Interquartile range (IQR)0.47569093

Descriptive statistics

Standard deviation0.35066194
Coefficient of variation (CV)0.05353707
Kurtosis-0.20146505
Mean6.5498904
Median Absolute Deviation (MAD)0.23648723
Skewness0.048720438
Sum56014.662
Variance0.12296379
MonotonicityNot monotonic
2023-11-01T18:59:06.621902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.535465388 27
 
0.3%
6.491058874 7
 
0.1%
6.342978493 7
 
0.1%
6.300293913 4
 
< 0.1%
6.429759212 4
 
< 0.1%
6.345054157 4
 
< 0.1%
6.269008771 4
 
< 0.1%
6.55810872 4
 
< 0.1%
6.615454909 4
 
< 0.1%
6.408128184 4
 
< 0.1%
Other values (7839) 8483
99.2%
ValueCountFrequency (%)
5.403812462 1
< 0.1%
5.509902804 1
< 0.1%
5.512348571 1
< 0.1%
5.513940925 1
< 0.1%
5.544676306 1
< 0.1%
5.549962091 1
< 0.1%
5.558572425 1
< 0.1%
5.562033603 1
< 0.1%
5.563070173 1
< 0.1%
5.564724316 1
< 0.1%
ValueCountFrequency (%)
7.63349248 1
< 0.1%
7.618428975 1
< 0.1%
7.616375776 1
< 0.1%
7.611164562 1
< 0.1%
7.593800538 1
< 0.1%
7.585348039 1
< 0.1%
7.575562315 1
< 0.1%
7.569528587 1
< 0.1%
7.556874884 1
< 0.1%
7.55625751 1
< 0.1%

Interactions

2023-11-01T18:58:50.533982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:40.073154image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:41.568092image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:43.002750image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:44.459025image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:46.202716image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:47.679672image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:49.082735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:50.739033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:40.286002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:41.752192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:43.221959image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:44.808213image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:46.397263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:47.869437image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:49.269451image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:50.907015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:40.460574image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:41.902575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:43.409145image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:44.996227image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:46.577797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:48.041192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:49.438209image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:51.082641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:40.642328image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:42.072687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:43.572247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:45.203321image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:46.762324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:48.221230image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:49.625836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:51.302788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:40.851559image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:42.256098image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:43.771869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:45.411074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:46.966833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:48.405690image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:49.824731image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:51.474384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:41.021053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:42.434497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:43.935811image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:45.636720image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:47.140940image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:48.574228image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:49.995330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:51.636295image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:41.194118image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:42.619582image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:44.098029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:45.813550image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:47.312806image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:48.739942image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:50.167469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:51.822043image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:41.378135image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:42.810592image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:44.271079image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:46.009940image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:47.495438image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:48.915370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-11-01T18:58:50.343536image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-11-01T18:59:06.828033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
PopularityVoteAverageVoteCountBudgetRunTimeRevenueYearweighted_averageOriginalLanguageNorth AmericaEuropeAsiaOceaniaSouth AmericaAfrica
Popularity1.0000.1280.3860.2490.0650.2930.1380.1340.0790.0990.0470.0350.0120.0000.000
VoteAverage0.1281.0000.238-0.0230.2860.085-0.0530.9890.1920.1340.0270.1400.0080.0210.028
VoteCount0.3860.2381.0000.6710.2900.721-0.2460.2370.4280.4240.0530.2930.0330.0770.032
Budget0.249-0.0230.6711.0000.3110.781-0.249-0.0290.3710.3830.0220.2110.0000.0580.000
RunTime0.0650.2860.2900.3111.0000.324-0.0550.2880.0980.0750.1190.1460.0000.0000.020
Revenue0.2930.0850.7210.7810.3241.000-0.3000.0800.3200.3360.1220.1450.0630.0540.000
Year0.138-0.053-0.246-0.249-0.055-0.3001.000-0.0580.1570.1910.1370.1100.0330.0520.023
weighted_average0.1340.9890.237-0.0290.2880.080-0.0581.0000.1780.1360.0320.1340.0080.0170.009
OriginalLanguage0.0790.1920.4280.3710.0980.3200.1570.1781.0000.7870.1460.5690.0720.1230.030
North America0.0990.1340.4240.3830.0750.3360.1910.1360.7871.0000.2910.4870.0170.0860.033
Europe0.0470.0270.0530.0220.1190.1220.1370.0320.1460.2911.0000.1770.0000.0000.026
Asia0.0350.1400.2930.2110.1460.1450.1100.1340.5690.4870.1771.0000.0220.0260.017
Oceania0.0120.0080.0330.0000.0000.0630.0330.0080.0720.0170.0000.0221.0000.0000.013
South America0.0000.0210.0770.0580.0000.0540.0520.0170.1230.0860.0000.0260.0001.0000.000
Africa\r\r0.0000.0280.0320.0000.0200.0000.0230.0090.0300.0330.0260.0170.0130.0001.000

Missing values

2023-11-01T18:58:52.102396image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-01T18:58:52.637689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OriginalLanguageOriginalTitleOverviewPopularityReleaseDateTitleVoteAverageVoteCountBudgetTagLineRunTimeRevenueGenresNorth AmericaEuropeAsiaOceaniaSouth AmericaAfricaYearweighted_average
Id
272051Inceptionskilled thief corporate espionage subconscious target chance regain old life payment task considered impossible inception implantation another person idea target subconscious4.4012162010-07-15Inception8.410.45241818.890684Your mind is the scene of the crime.14820.531540Action, ScienceFiction, Adventure11000020107.611165
4976981Black Widowalso known black widow part ledger dangerous conspiracy tie past force stop nothing bring must deal history spy broken relationship left wake long avenger4.3995962021-07-07Black Widow7.39.14291819.113828Her world. Her secrets. Her legacy.13419.755027Action, Adventure, ScienceFiction10000020216.951345
6031The Matrixcentury matrix tell story computer hacker join group underground insurgent fighting vast powerful computer rule earth4.3939801999-03-30The Matrix8.210.08435017.958645Welcome to the Real World.13619.954354Action, ScienceFiction10000019997.481176
8437940정이uninhabitable earth outcome civil war hinge brain elite soldier create robot mercenary4.3938192023-01-12JUNG_E6.26.2383250.000000AI Combat Warrior Will be Unleashed.980.000000ScienceFiction00100020236.384944
4468931Trolls World Tourqueen poppy branch make surprising discovery — troll world beyond distinct difference create big clash various tribe mysterious threat put troll across land danger poppy branch band friend must embark epic quest create harmony among troll unite certain doom4.3934362020-03-11Trolls World Tour7.37.56734618.315320Happiest. Movie. Ever.9017.712964Family, Animation, Comedy, Fantasy, Adventure, Music10000020206.915282
7298540콘크리트 유토피아world reduced rubble massive earthquake one know sure far ruin stretch because earthquake may heart one apartment building left standing apartment4.3908622023-08-09Concrete Utopia7.62.7080500.000000We believe we are chosen1300.000000Thriller, ScienceFiction, Drama00100020236.813379
240211The Twilight Saga: Eclipsefind surrounded danger string mysterious killing malicious vampire quest revenge midst forced choose love friendship knowing decision potential ignite ageless struggle vampire werewolf graduation quickly approaching important decision life4.3859072010-06-23The Twilight Saga: Eclipse6.29.01893818.035018It all begins... with a choice.12420.364433Adventure, Fantasy, Drama, Romance10000020106.354121
18801Red Dawndawn world war group band together defend town — and country — from soviet force4.3849721984-08-10Red Dawn6.36.55250816.648724In our time, no foreign army has ever occupied American soil. Until now.11417.462956Action, Thriller, War, Drama10000019846.426945
42571Scary Movie 4find house life little boy go quest find also alien tripod world uncover secret order stop4.3849352006-04-12Scary Movie 45.58.00770017.622173Bury the grudge. Burn the village. See the saw.8318.998768Comedy10000020066.006412
286760Schiave bianche: violenza in Amazzoniayoung woman seek vengeance find love parent taken prisoner indigenous tribe4.3835751985-08-09Amazonia: The Catherine Miles Story6.15.0369530.000000Only one thing kept her alive.900.000000Adventure, Drama, Horror01000019856.362782
OriginalLanguageOriginalTitleOverviewPopularityReleaseDateTitleVoteAverageVoteCountBudgetTagLineRunTimeRevenueGenresNorth AmericaEuropeAsiaOceaniaSouth AmericaAfricaYearweighted_average
Id
110921Presumed Innocentrusty deputy prosecutor engaged obsessive affair soon he accused crime fight clear name becomes whirlpool lie hidden passion2.5690951990-07-27Presumed Innocent6.86.38856116.906553Some people would kill for love12719.215044Mystery, Crime, Thriller10000019906.655719
6644231The Windermere Childrenstory project rehabilitate child survivor holocaust shore lake2.5689412020-01-27The Windermere Children7.54.5643480.000000NaN880.000000Drama, TvMovie, History01000020206.895458
30771Son of Frankensteinone son late henry find father ghoulish creation coma find monster bent revenge2.5689411939-01-13Son of Frankenstein6.75.32301012.948010The black shadows of the past bred this half-man . . . half-demon ! . . . creating a new and terrible juggernaut of destruction !990.000000Horror, ScienceFiction10000019396.602898
4135430Dear Zindagiunconventional thinker help budding cinematographer gain new perspective life2.5688652016-11-23Dear Zindagi7.15.34710815.274126NaN15115.032313Drama, Romance00100020166.767451
144000Largo Winchpowerful billionaire secret adoptive son must race prove legitimacy find father killer stop taking financial empire2.5688652008-12-17The Heir Apparent: Largo Winch6.06.18620917.050762NaN1080.000000Adventure, Drama, Action, Thriller01100020086.296317
2749115 Minuteseastern criminal come new york city pick share score steal video camera start activity legal illegal learn medium circus make remorseless killer look like victim make rich target homicide detective fire marshal warsaw cop investigating murder former criminal partner everything sell local tabloid show top story2.5688652001-03-0115 Minutes5.96.47080017.909855America Likes to Watch12017.847270Action, Crime, Thriller11000020016.244575
111281Ladder 49watchful eye mentor captain mike probationary jack seasoned veteran fire station however jack crossroad sacrifice he made put harm way innumerable time significantly impacted relationship wife2.5687882004-10-01Ladder 496.46.56103117.909855Their greatest challenge lies in rescuing one of their own11518.126869Drama, Action, Thriller10000020046.472989
4844820Le Grand Bainsuffering depression last two year barely able keep head water despite medication gulp day every day wife encouragement unable find meaning life curiously end finding sense purpose swimming pool joining swimming team2.5687122018-10-24Sink or Swim6.97.2427980.000000NaN1220.000000Drama, Comedy01000020186.712571
4537551Arcticman arctic finally receive long rescue however tragic accident opportunity lost must decide whether remain relative safety camp embark deadly trek unknown potential salvation2.5687122018-11-21Arctic6.56.99851014.508658Survival is the only option9815.226498Drama11000020186.518539
545181Justin Bieber: Never Say Nevertell story canada hair smile voice chronicle unprecedented rise fame way street canada video selling square garden new york headline act world tour feature usher scooter la men cyrus smith family member part crew huge mix interview guest performance theater around world highest concert movie time beating previous record2.5687122011-02-11Justin Bieber: Never Say Never5.25.93489416.380460Find out what's possible if you never give up.10518.405567Music, Documentary, Family10000020115.952678